[relay][op] add expand op (from ONNX) to relay frontend

python/tvm/relay/frontend/onnx.py

@@ -1080,6 +1080,52 @@ class Or(Elemwise):
     def _impl_v7(cls, inputs, attr, params):
         return _op.logical_or(inputs[0], inputs[1])
 
+class Expand(OnnxOpConverter):
+    """ Operator converter for Expand.
+    """
+    @classmethod
+    def _impl_v8(cls, inputs, attr, params):
+        in_shape = np.array(infer_shape(inputs[0])).astype('int32')
+        if get_name(inputs[1]) in params:
+            shape = params[inputs[1].name_hint].asnumpy().astype('int32')
+        else:
+            shape = infer_value_simulated(inputs[1], params).asnumpy().astype('int32')
+
+        # Currently 'op.broadcast_to' expect the rank of the given 'shape'
+        # (the 2nd input) is always higher than that of the given 'input' (the 1st input)
+        # However, ONNX Expand supports multi-directional broadcasting, which allows
+        # above pattern and also some extent of 'shape' can be smaller than the corresponding
+        # extent of 'input'. In this case, the extent of 'shape' must be 1.
+        # https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md
+        # In above cases, we cannot directorly apply 'op.broadcast_to' instead of 'expand'
+        # so, here we solved this problem by expanding the given 'shape' itself.
+        def expand_shape(in_shape, shape):
+            """ A function expands the shape when the rank is lower than that of the given
+            intput. Also it replaces the extent of the shape with the corresponding extent
+            of the intput when it is 1.
+            """
+
+            # here we flip the shapes because this can be more simply written
+            # when the innermost dimension is located at the index 0.
+            in_shape = np.flip(in_shape, axis=0)
+            shape = np.flip(shape, axis=0)
+
+            if in_shape.size < shape.size:
+                for i in range(shape.size):
+                    if i < in_shape.size and in_shape[i] > shape[i]:
+                        shape[i] = in_shape[i]
+            else:
+                for i in range(in_shape.size):
+                    if i >= shape.size:
+                        np.append(shape, in_shape[i])
+                    elif shape[i] == 1:
+                        shape[i] = in_shape[i]
+
+            new_shape = np.flip(shape, axis=0)
+            return new_shape
+
+        shape = expand_shape(in_shape, shape)
+        return _op.broadcast_to(inputs[0], shape=tuple(shape))
 
 # compatible operators that do NOT require any conversion.
 _identity_list = []
@@ -1187,6 +1233,7 @@ def _get_convert_map(opset):
         # defs/tensor
         'Cast': Cast.get_converter(opset),
         'Reshape': Reshape.get_converter(opset),
+        'Expand': Expand.get_converter(opset),
         'Concat': Concat.get_converter(opset),
         'Split': Split.get_converter(opset),
         'Slice': Slice.get_converter(opset),

tests/python/frontend/onnx/test_forward.py

@@ -142,6 +142,46 @@ def test_reshape():
     tvm.testing.assert_allclose(ref_shape, tvm_out.shape)
 
 
+def test_expand():
+
+    def _test_expand(name, data, shape, ref_data):
+        shape_array = np.array(shape)
+        shape_node = onnx.helper.make_node('Constant',
+                                    inputs=[],
+                                    outputs=['shape'],
+                                    value=onnx.helper.make_tensor(name = 'const_tensor',
+                                                                  data_type = onnx.TensorProto.INT32,
+                                                                  dims = shape_array.shape,
+                                                                  vals = shape_array.flatten().astype('int32')))
+        expand_node = helper.make_node("Expand", ["in", "shape"], ["out"])
+
+        graph = helper.make_graph([shape_node, expand_node],
+                                "expand_test",
+                                inputs = [helper.make_tensor_value_info("in",
+                                                TensorProto.FLOAT, list(data.shape))],
+                                outputs = [helper.make_tensor_value_info("out",
+                                                TensorProto.FLOAT, list(ref_data.shape))])
+
+        model = helper.make_model(graph, producer_name=name)
+
+        for target, ctx in ctx_list():
+            tvm_out = get_tvm_output(model, data, target, ctx, ref_data.shape, 'float32')
+
+        tvm.testing.assert_allclose(ref_data, tvm_out)
+
+    in_shape = (3, 1)
+    shape = (3, 4)
+    data = np.random.uniform(size=in_shape).astype(np.float32)
+    ref_data = np.tile(data, 4)
+    _test_expand('expand_with_dim_unchanged_test', data, shape, ref_data)
+
+    in_shape = (3, 1)
+    shape = (2, 1, 6)
+    data = np.random.uniform(size=in_shape).astype(np.float32)
+    ref_data = data * np.ones(shape, dtype=np.float32)
+    _test_expand('expand_with_dim_changed_test', data, shape, ref_data)
+
+
 def verify_depth_to_space(inshape, outshape, mode, blockSize):
     node = onnx.helper.make_node('DepthToSpace',
                                  inputs=['x'],
@@ -1710,6 +1750,7 @@ def test_or():
     test_flatten()
     test_reshape()
     test_shape()
+    test_expand()
     test_power()
     test_squeeze()
     test_unsqueeze()